MEC federated learning method, device, and computer-readable storage medium

Abstract

The embodiment of the present application provides a method, device and computer-readable storage medium for MEC federated learning. The method comprises: when any multi-access edge computing MEC system in any federated learning group issues a federated learning request, based on the resource capacity utilization of each MEC system in the federated learning group, determining a MEC system coordinator from each MEC system; receiving model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtaining the resource capacity utilization of the MEC system coordinator in real time during the receiving process; then determining whether to switch the coordinator based on the resource capacity utilization of the MEC system coordinator, aggregating each model information through the MEC system coordinator, and sending the aggregated model information to each MEC system in the federated learning group. This solution realizes MEC federated learning, ensures the performance of the MEC system coordinator, and realizes balanced resource capacity utilization of the MEC system within the MEC system federated learning group.

Description

Technical Field

[0001] The present application relates to the field of 5G edge computing technology. Specifically, the present application relates to a MEC federated learning method, device and computer-readable storage medium. Background Art

[0002] Multi-access Edge Computing (MEC) is a natural progression from the evolution of mobile base stations and the convergence of IT and telecommunications networks. By deploying various services and caching content at the network edge, it can further alleviate congestion in the mobile core network and enable efficient local services. MEC provides a new ecosystem and value chain, enabling operators to open their radio access network (RAN) edge to authenticated and certified third parties, enabling flexible and rapid deployment to mobile users, enterprises, and vertical industries, offering innovative applications and services such as video analytics, location-based services, enhanced display, local content distribution, and data caching.

[0003] Federated learning was born to address issues such as data fragmentation, data isolation, user privacy leakage, and data shortage faced by machine learning. Federated learning is a distributed machine learning framework that allows multiple participants to protect their own data in their local private locations while collaborating and securely establishing federated learning models under the requirements of user privacy protection, data security, and government regulations. Its technology can effectively solve the problem of data silos and realize intelligent cooperation among participants.

[0004] Based on MEC's real-time, agile, intelligent, and secure features, mobile network operators, enterprises, and vertical industries have joined the MEC queue and established MEC systems. Communication between different MEC systems is an essential requirement in the edge computing industry and ecosystem today and in the future. The federation of MEC systems enables the shared use of MEC services and applications. New features are provided by collaborating with other services rather than developing all services. For example, speech recognition can serve as a key function for other services (such as navigation applications). In this case, the speech recognition service provider is not necessarily the same as the navigation service provider. Each service can be deployed on different MEC systems in the MEC environment.

[0005] However, there is currently no mature MEC federated learning solution, so there is an urgent need to provide a new MEC federated learning solution. Summary of the Invention

[0006] The purpose of this application is to solve at least one of the above technical deficiencies. The technical solutions provided by the embodiments of this application are as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for MEC federated learning, including:

[0008] When any multi-access edge computing MEC system in any federated learning group sends a federated learning request, the MEC system coordinator is determined from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group;

[0009] Receive model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtain the resource capacity utilization of the MEC system coordinator in real time during the receiving process;

[0010] If the resource capacity utilization rate of the MEC system coordinator does not exceed the first preset threshold during the receiving process, the MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group respectively;

[0011] If the resource capacity utilization rate of the MEC system coordinator exceeds the first preset threshold value during the receiving process, a new MEC system coordinator is determined from each MEC system and switched to the new MEC system coordinator to receive each model information. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization rate of the new MEC system coordinator does not exceed the first preset threshold value during the receiving process. After the model information is aggregated by the new MEC system coordinator, the aggregated model information is sent to each MEC system in the federated learning group respectively.

[0012] In an optional embodiment of the present application, based on the resource capacity utilization of each MEC system in the federated learning group, determining the MEC system coordinator from each MEC system includes:

[0013] Obtain a preset set of available MEC system coordinators, where each MEC system in the preset set of available MEC system coordinators carries a corresponding priority;

[0014] Among the MEC systems in the federated learning group that belong to the set of available MEC system coordinators, the MEC system with a resource capacity utilization rate not exceeding a second preset threshold and the highest priority is determined as the MEC system coordinator, and the second preset threshold is not greater than the first preset threshold.

[0015] In an optional embodiment of the present application, the method further includes:

[0016] If the resource capacity utilization rates of all MEC systems in the federated learning group that belong to the set of available MEC system coordinators exceed the second preset threshold, the MEC system with the lowest resource capacity utilization rate among the MEC systems in the federated learning group that do not belong to the set of available MEC system coordinators will be determined as the MEC system coordinator.

[0017] In an optional embodiment of the present application, obtaining a preset set of available MEC system coordinators includes:

[0018] Obtain multiple federated learning groups from a preset number of MEC systems;

[0019] The MEC systems that appear in any federated learning group are determined as elements of the available MEC coordinator set, and the available MEC coordinator set is constructed. The priority of each MEC system in the MEC coordinator set is proportional to the number of times it appears in each federated learning group.

[0020] In an optional embodiment of the present application, a plurality of federated learning groups are obtained from a preset number of MEC systems, including:

[0021] Identify MEC systems with the same data source as a federated learning group; and / or,

[0022] The MEC systems whose model feature similarity meets the preset conditions are identified as a federated learning group.

[0023] In an optional embodiment of the present application, the MEC system coordinator receives model information sent by other MEC systems in the federated learning group, including:

[0024] Determine whether the federated learning group includes an existing MEC system coordinator;

[0025] If not included, the model information sent by other MEC systems in the federated learning group will be directly received through the MEC system coordinator;

[0026] If included, the model information received by the existing MEC system coordinator is obtained through the MEC system coordinator, and the model information of other MEC systems is continued to be received through the MEC system.

[0027] In an optional embodiment of the present application, determining a new MEC system coordinator from each MEC system and switching to the new MEC system coordinator to receive each model information includes:

[0028] Based on the resource capacity utilization of each MEC system in the federated learning group, a new MEC system coordinator is determined from each MEC system;

[0029] The model information received by the MEC system coordinator is obtained through the new MEC system coordinator, and the model information of other MEC systems is continued to be received through the new MEC system.

[0030] In a second aspect, an embodiment of the present application provides a MEC federated learning device, including:

[0031] The MEC system coordinator determination module is used to determine the MEC system coordinator from each MEC system based on the resource capacity utilization of each MEC system in any federated learning group when any multi-access edge computing MEC system in any federated learning group sends a federated learning request;

[0032] The model information receiving module is used to receive model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtain the resource capacity utilization of the MEC system coordinator in real time during the receiving process;

[0033] A first model information aggregation module is configured to aggregate the model information through the MEC system coordinator if the resource capacity utilization rate of the MEC system coordinator does not exceed a first preset threshold during the reception process, and then send the aggregated model information to each MEC system in the federated learning group;

[0034] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor;

[0035] The memory stores a computer program;

[0036] A processor is used to execute a computer program to implement the method provided in the embodiment of the first aspect or any optional embodiment of the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in the embodiment of the first aspect or any optional embodiment of the first aspect is implemented.

[0038] In a fifth aspect, embodiments of the present application provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, so that when executed by the computer device, the method provided in the embodiment of the first aspect or any alternative embodiment of the first aspect is implemented.

[0039] The beneficial effects of the technical solution provided by this application are:

[0040] The MEC system coordinator is determined based on the resource capacity utilization of each MEC system in the group that initiates the federated learning request. Subsequently, when the MEC system coordinator receives model information from other MEC systems, the resource capacity utilization of the MEC system coordinator is obtained in real time. The resource capacity utilization is then used to determine whether to switch the MEC system coordinator. This solution implements MEC federated learning and considers resource capacity utilization during the MEC system coordinator confirmation process, ensuring the performance of the MEC system coordinator and achieving balanced resource capacity utilization across MEC systems within the MEC system federated learning group. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments of the present application.

[0042] Figure 1 A flowchart of a MEC federated learning method provided in an embodiment of the present application;

[0043] Figure 2 This is a flowchart of an example of an embodiment of the present application for detecting that a requested service is not within the MEC system;

[0044] Figure 3 This is a flowchart of federated learning when no handover occurs in the MEC system coordinator in an example of an embodiment of the present application;

[0045] Figure 4 This is a federated learning flow chart in which a switching coordinator is located in a set of available MEC system coordinators in an example of an embodiment of the present application;

[0046] Figure 5 A flowchart of federated learning in which a switched coordinator is not located in a set of available MEC system coordinators in an example of an embodiment of the present application;

[0047] Figure 6 This is an interaction diagram of the MEC system coordinator handover in an example of an embodiment of the present application;

[0048] Figure 7 This is a federated learning interaction diagram when no handover occurs in the MEC system coordinator in an example of an embodiment of the present application;

[0049] Figure 8 This is a federated learning interaction diagram when the MEC system coordinator switches in an example of an embodiment of the present application.

[0050] Figure 9 A schematic diagram of the structure of a MEC federated learning device provided in an embodiment of the present application;

[0051] Figure 10A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0053] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0054] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0055] Figure 1 A flow chart of a MEC federated learning method provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method may include:

[0056] Step S101: When any multi-access edge computing MEC system in any federated learning group issues a federated learning request, the MEC system coordinator is determined from each MEC system based on the number of MEC systems appearing in each federated learning group and the resource capacity utilization.

[0057] Specifically, when any MEC system in any federated learning group issues a federated learning request, the MEC federated learning method provided in the embodiment of the present application is triggered. Then, the resource capacity utilization of each MEC system in the federated learning group is obtained, and the MEC system that serves as the MEC system coordinator is determined based on the resource capacity utilization of each MEC system. The MEC system coordinator will serve as the execution entity that aggregates the federated learning participants in the federated learning, where the federated learning participants are the MEC systems in the federated learning group.

[0058] It can be understood that in the solution of the present application, when selecting the MEC system coordinator from the federated learning group, in order to ensure the performance of the MEC system coordinator, the resource capacity utilization of each MEC system in the federated learning group needs to be considered.

[0059] It should be noted that the MEC system consists of the MEC host and MEC management. The MEC host is an entity that includes the MEC platform and virtualized infrastructure, providing computing, storage, and network resources for running MEC applications. The MEC platform is the set of basic functions required to run MEC applications on a specific virtualized infrastructure and enable them to provide and consume MEC services. The MEC platform can also provide services. MEC applications are instantiated on the MEC host's virtualized infrastructure based on configurations or requests validated by the MEC management layer. MEC management includes MEC system-level management and MEC host-level management. MEC system-level management includes the Multi-Access Edge Orchestrator as its core component, which provides an overview of the entire MEC system. MEC host-level management includes the MEC Platform Manager and the Virtualized Infrastructure Manager, responsible for managing MEC-specific functions for specific MEC hosts and the applications running on them. The Mp1 interface is the interface between the MEC platform and MEC applications, the Mm5 interface is the interface between the MEC Platform Manager and the MEC platform, and the Mm3 interface is the interface between the MEC Orchestrator and the MEC Platform Manager.

[0060] When an instantiated MEC application in a MEC system in a federated learning group initiates a service request, it is detected that this service does not exist in the MEC system, such as Figure 2 The specific detection process is as follows:

[0061] 1. The service consumer (i.e., the MEC application instantiated in the MEC system) sends a request to the MEC platform through the Mp1 reference point using its ID to request the required service.

[0062] 2. The corresponding MEC platform in the MEC system finds that the requested service is not available locally.

[0063] 3. Forward the service request to the MEC platform manager of the MEC system through the Mm5 interface.

[0064] 4. The MEC platform manager forwards the service request to the MEC orchestrator in sequence through the Mm3 interface.

[0065] 5. The MEC orchestrator detects the topology and available services of the MEC system and finds that the requested service is not available in the MEC system.

[0066] Step S102: The MEC system coordinator receives model information sent by other MEC systems in the federated learning group, and obtains the resource capacity utilization of the MEC system coordinator in real time during the receiving process.

[0067] Specifically, the MEC system coordinator determined in the previous step receives model information sent by other MEC systems in the federated learning group. After receiving the model information of each MEC system, the model information can be aggregated to obtain the aggregated model information, completing federated learning. However, in order to further ensure the performance of the MEC system coordinator, it is necessary to monitor its resource capacity utilization in real time during the reception of its data model information. When the resource capacity utilization exceeds a first preset threshold, the current MEC system coordinator is deactivated, a new MEC system coordinator is determined, and the current MEC system coordinator is switched to the new MEC system coordinator. The subsequent steps for switching the MEC system coordinator will be described in detail.

[0068] In step S103, if the resource capacity utilization rate of the MEC system coordinator does not exceed the first preset threshold during the receiving process, the MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group.

[0069] In one scenario, if the resource capacity utilization of the current MEC system coordinator does not exceed a first preset threshold, that is, after the current MEC system coordinator receives all model information, its resource capacity utilization is no greater than the first preset threshold, then its performance can be guaranteed. The MEC system coordinator can further aggregate the model information and send the aggregated model information to each MEC system in the federated learning group. After receiving the aggregated model information, each MEC system updates its local model information.

[0070] It should be noted that the first preset threshold can be set according to experience and actual needs, for example, set to 60%.

[0071] Step S104: If the resource capacity utilization of the MEC system coordinator exceeds the first preset threshold during the receiving process, a new MEC system coordinator is determined from each MEC system and switched to receiving each model information through the new MEC system coordinator. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization of the new MEC system coordinator does not exceed the first preset threshold during the receiving process. After the model information is aggregated through the new MEC system coordinator, the aggregated model information is sent to each MEC system in the federated learning group.

[0072] In another scenario, if the resource capacity utilization of the current MEC system coordinator exceeds the first preset threshold, that is, after the current MEC system coordinator receives all model information, its resource capacity utilization is greater than the first preset threshold, and its performance may not be guaranteed. Therefore, it is necessary to re-determine a new MEC coordinator, have the new MEC system coordinator aggregate the model information, and then send the aggregated model information to each MEC system in the federated learning group.

[0073] Specifically, the method of re-determining the new MEC system coordinator can be the same as that in step S101, and the resource capacity utilization of each MEC system in the federated learning group is also considered. This process is repeated, that is, each time a new MEC system coordinator is determined, the previous MEC system coordinator is switched to the new MEC system coordinator, and the model information is received through the new MEC system coordinator. Similarly, the resource capacity utilization of the new MEC system coordinator during the docking process is monitored in real time. If it still exceeds the first preset threshold, the new MEC system coordinator is determined again, and the resource capacity utilization during the switching and receiving process is monitored. The above steps of "determining the new MEC system coordinator-switching-receiving process monitoring" can be repeated multiple times until the resource capacity utilization of the new MEC system coordinator finally determined does not exceed the first preset threshold after receiving all model information.

[0074] The solution provided in this application determines the MEC system coordinator based on the resource capacity utilization of each MEC system in the group that initiates the federated learning request. In the subsequent process of receiving model information of other MEC systems through the MEC system coordinator, the resource capacity utilization of the MEC system coordinator during the reception process is obtained in real time, and the MEC system coordinator is again determined based on the resource capacity utilization whether to switch the MEC system coordinator. This solution implements MEC federated learning and takes resource capacity utilization into account during the MEC system coordinator confirmation process, thereby ensuring the performance of the MEC system coordinator and achieving balanced resource capacity utilization of the MEC system within the MEC system federated learning group.

[0075] In an optional embodiment of the present application, based on the resource capacity utilization of each MEC system in the federated learning group, determining the MEC system coordinator from each MEC system includes:

[0076] Obtain a preset set of available MEC system coordinators, where each MEC system in the preset set of available MEC system coordinators carries a corresponding priority;

[0077] Among the MEC systems in the federated learning group that belong to the set of available MEC system coordinators, the MEC system with a resource capacity utilization rate not exceeding a second preset threshold and the highest priority is determined as the MEC system coordinator, and the second preset threshold is not greater than the first preset threshold.

[0078] The process of obtaining the preset available MEC system coordinator set includes:

[0079] Obtain multiple federated learning groups from a preset number of MEC systems;

[0080] The MEC systems that appear in any federated learning group are determined as elements of the available MEC coordinator set, and the available MEC coordinator set is constructed. The priority of each MEC system in the MEC coordinator set is proportional to the number of times it appears in each federated learning group.

[0081] Furthermore, multiple federated learning groups are obtained from a preset number of MEC systems, including:

[0082] Identify MEC systems with the same data source as a federated learning group; and / or,

[0083] The MEC systems whose model feature similarity meets the preset conditions are identified as a federated learning group.

[0084] Specifically, first, assuming there are m MEC systems, p MEC system federated learning groups are formed based on the same data source of the business model or based on the similarity of business model features.

[0085] The term "samples from the same source" means that different MEC systems may have different model samples, and the data for different AI model samples originates from the same physical device. For example, one MEC system may have an image recognition model, and another may have a speech recognition model. Although the models in these systems are different, the data for these two models originates from the same physical terminal device. Model feature similarity means that each MEC system has different models, but all models share the same feature attributes. For example, different MEC systems may have image recognition models from different visual terminals. Although the collected image / video sample data originates from different sources, the model samples share the same feature attributes.

[0086] Then, count the number of times N that each MEC system is repeated in the p MEC system federated learning groups, and then calculate the weight value of each MEC system in the p MEC system groups as R = N / p. The weight value represents the importance of the MEC system in the p MEC system groups. The larger the weight value, the more model information the MEC system has, and the more useful the federated learning model training is, the better the efficiency of federated learning.

[0087] Finally, based on the MEC system weight values R calculated in the above steps, sort the weight values R from large to small, and select the corresponding MEC systems with weight values R greater than 0. After the weight values R are sorted, the corresponding MEC system set is {MEC1, MEC2..., MEC n ; n<m}, the MEC system set is defined as the set of available MEC system coordinators, and the priority of the MEC systems contained therein is also determined. The larger the weight value, the greater the priority of the MEC system.

[0088] As can be seen from the foregoing description, the MEC federated learning scheme of the present application can be divided into two situations: one is that the MEC system coordinator does not switch after the MEC system coordinator is determined. The other is that the MEC system coordinator switches after the MEC system coordinator is determined. In this case, it also includes: if the resource capacity utilization rates of all MEC systems belonging to the set of available MEC system coordinators in the federated learning group exceed the second preset threshold, then the MEC system with the smallest resource capacity utilization rate among the MEC systems that do not belong to the set of available MEC system coordinators in the federated learning group is determined as the MEC system coordinator. The two situations will be described in detail below.

[0089] like Figure 3 As shown, the solution for the MEC system coordinator to not switch (no switching) may include the following steps:

[0090] 1. The MEC system in the MEC federated learning group initiates a federated learning request.

[0091] 2. Assume that the first preset threshold and the second preset threshold of the MEC system resource capacity utilization are C1 and C2 respectively, and select the MEC system from the federated learning group according to the priority of the set of available MEC system coordinators.

[0092] 3. Determine whether the resource capacity utilization of the currently selected MEC system exceeds the second preset threshold C2.

[0093] If the resource capacity utilization of the currently selected MEC system exceeds the second preset threshold C2, return to step 2 and select other MEC systems from the federated learning group again according to the priority of the set of available MEC system coordinators.

[0094] If the currently selected MEC system does not exceed the second preset threshold C2, the currently selected MEC system is determined to be the MEC system coordinator.

[0095] 4. Each MEC system participant encrypts the local computing model information and sends it to the MEC system coordinator. The model information includes model characteristics, model parameters and other information.

[0096] 5. During the process of sending the model information, the resource capacity utilization of the MEC system coordinator is monitored in real time. If the resource capacity utilization of the MEC system coordinator is always less than the first preset threshold C1, then no switching occurs in the MEC system coordinator.

[0097] 6. The MEC system coordinator aggregates the received model information and then sends the aggregated model information to each MEC system participant.

[0098] 7. Each MEC participant receives the aggregated model information and updates the local model information.

[0099] like Figure 4 As shown in FIG, a scheme in which the MEC system coordinator is switched and the new MEC system coordinator is located in the set of available MEC system coordinators may include the following steps:

[0100] 1. The MEC system in the MEC federated learning group initiates a federated learning request.

[0101] 2. Assume that the first preset threshold and the second preset threshold of the MEC system resource capacity utilization are C1 and C2 respectively, and select the MEC system from the federated learning group according to the priority of the set of available MEC system coordinators.

[0102] 3. Determine whether the resource capacity utilization of the currently selected MEC system exceeds the second preset threshold C2.

[0103] If the resource capacity utilization of the currently selected MEC system exceeds the second preset threshold C2, return to step 2 and select a MEC system from the federated learning group again according to the priority of the set of available MEC system coordinators.

[0104] If the resource capacity utilization of the currently selected MEC system does not exceed the second preset threshold C2, it is determined whether the MEC system federated learning group includes an existing MEC system coordinator.

[0105] Furthermore, if the MEC system federated learning group does not include an existing MEC system coordinator, the selected MEC system is determined to be the MEC system coordinator.

[0106] If the MEC system federated learning group contains an existing MEC system coordinator, the selected MEC system is determined to be the coordinator of the MEC system switch, and the existing MEC system coordinator is switched to the determined MEC system coordinator.

[0107] 4. Each MEC system participant encrypts the local computing model information and sends it to the MEC system coordinator. The model information includes model characteristics, model parameters and other information.

[0108] 5. During the model information transmission process, monitor the resource capacity utilization of the MEC system coordinator in real time.

[0109] 6. Determine whether the resource capacity utilization of the MEC system coordinator exceeds a first preset threshold C1.

[0110] If the resource capacity utilization of the MEC system coordinator exceeds the first preset threshold C1, the process returns to step 2.

[0111] If the resource capacity utilization rate of the MEC system coordinator does not exceed the first preset threshold value C1, the MEC system coordinator aggregates the received model information and then sends the aggregated model information to each MEC system participant.

[0112] 7. Each MEC participant receives the aggregated model information and updates the local model information.

[0113] like Figure 5 As shown in FIG, a solution in which the MEC system coordinator is switched and the new MEC system coordinator is not in the set of available MEC system coordinators may include the following steps:

[0114] 1. The MEC system in the MEC federated learning group initiates a federated learning request.

[0115] 2. Assume that the first preset threshold and the second preset threshold of the MEC system resource capacity utilization are C1 and C2 respectively, and select the MEC system from the federated learning group according to the priority of the available MEC system coordinators.

[0116] 3. Determine whether the resource capacity utilization of the currently selected MEC system exceeds the second preset rate threshold C2.

[0117] If the resource capacity utilization of the currently selected MEC system exceeds the second preset threshold C2, it is determined whether the resource capacity utilization of the MEC systems belonging to the preset available MEC coordinators in the federated learning group are all greater than C2.

[0118] If the resource capacity utilization rates of all the above MEC systems in the federated learning group are greater than C2, then the MEC system with the minimum resource capacity utilization rate is selected from the available MEC system coordination set, and step 4 is executed to determine whether the federated learning group contains an existing MEC system coordinator.

[0119] If the resource capacity utilization of the above MEC systems in the federated learning group is not all greater than C2, then return to step 2 and select a MEC system from the federated learning group again according to the priority of the set of available MEC system coordinators.

[0120] 4. If the resource capacity utilization of the currently selected MEC system does not exceed the second preset threshold C2, determine whether the federated learning group includes an existing MEC system coordinator.

[0121] If the federated learning group does not include an existing MEC system coordinator, the currently selected MEC system is determined to be the MEC system coordinator.

[0122] If the federated learning group contains an existing MEC system coordinator, the selected MEC system is determined as the coordinator for the MEC system switching, and the existing MEC system coordinator is switched to the currently selected MEC system coordinator.

[0123] 5. Each MEC system participant encrypts the local computing model information and sends it to the MEC system coordinator. The model information includes model characteristics, model parameters and other information.

[0124] 6. During the model information transmission process, monitor the resource capacity utilization of the MEC system coordinator in real time.

[0125] 7. Determine whether the resource capacity utilization of the MEC system coordinator exceeds a first preset threshold C1.

[0126] If the resource capacity utilization of the MEC system coordinator exceeds the first preset threshold C1, the process returns to step 2 and selects a MEC system from the federated learning group again according to the priority of the available MEC system coordinators.

[0127] If the resource capacity utilization rate of the MEC system coordinator does not exceed the first preset threshold C1, the MEC system coordinator aggregates the received model information and then sends the aggregated model information to each MEC system participant.

[0128] 8. Each MEC participant receives the aggregated model information and updates the local model information.

[0129] In an optional embodiment of the present application, determining a new MEC system coordinator from each MEC system and switching to receiving each model information through the new MEC system coordinator includes:

[0130] Based on the resource capacity utilization of each MEC system in the federated learning group, a new MEC system coordinator is determined from each MEC system;

[0131] The model information received by the MEC system coordinator is obtained through the new MEC system coordinator, and the model information of other MEC systems is continued to be received through the new MEC system.

[0132] Among them, when the MEC system in the federated learning group initiates a federated learning request, the MEC system coordinator 1 has been determined, and the other participants in the federated learning group transmit the model to the MEC system coordinator 1. At this time, it is determined that the MEC system coordinator needs to be switched, such as Figure 6 As shown, the process can include the following steps:

[0133] 1. During the model information transmission process, the resource capacity utilization of MEC system coordinator 1 reaches the first preset threshold C1.

[0134] 2. The MEC system coordinator 1 will notify each participant to stop sending model information.

[0135] 3. MEC system coordinator 1 determines MEC system coordinator 2 (i.e., the new MEC system coordinator) based on the method for determining the MEC system coordinator.

[0136] 4. MEC system coordinator 1 sends a handover request to MEC system coordinator 2.

[0137] 5. MEC system coordinator 2 sends a response to MEC system coordinator 1, agreeing to the handover request.

[0138] 6. MEC system coordinator 1 sends the received model information and its own model information to MEC system coordinator 2.

[0139] 7. The MEC system coordinator 1 will notify each participant that the coordinator has changed.

[0140] 8. Each participant switches to the coordinator 2 of the MEC system and continues to send the model.

[0141] The solution of this application is further explained below through two examples. Figure 7 As shown, the MEC system coordinator does not switch, and the MEC system federated learning method may include:

[0142] 1. The MEC application initiates a service request and detects that the service does not exist in the MEC system where the MEC is located, which triggers federated learning.

[0143] 2. In the established federated learning group, determine the MEC system coordinator 1 in the MEC system according to the method for determining the MEC system coordinator.

[0144] 3. MEC system coordinator 1 sends a federated learning request to each participant in the federated learning group.

[0145] 4. Each participant of the MEC system sends model information to the MEC system coordinator 1. The model information includes model characteristics, model parameters and other information.

[0146] 5. MEC system coordinator 1 aggregates the model information sent by each participant.

[0147] 6. MEC system coordinator 1 sends the aggregated model information to each participant.

[0148] 7. Each MEC system participant updates local model information.

[0149] like Figure 7 As shown, the MEC system coordinator switches, and the MEC system federated learning method may include:

[0150] 1. The MEC application initiates a service request and detects that the service does not exist in the MEC system where the MEC is located, which triggers federated learning.

[0151] 2. In the established federated learning group, determine the MEC system coordinator 1 in the MEC system based on the MEC system coordinator.

[0152] 3. MEC system coordinator 1 sends a federated learning request to each participant in the federated learning group.

[0153] 4. Each MEC system participant sends model information to the MEC system coordinator 1. The model information includes model type and model parameter information.

[0154] 5. During the model information transmission process, the MEC system coordinator is switched. According to the second method for determining the MEC system coordinator, MEC system coordinator 2 is determined.

[0155] 6. According to the MEC system coordinator switching method, the MEC system coordinator will be switched from MEC system coordinator 1 to MEC system coordinator 2.

[0156] 7. Each MEC system participant sends model information to the MEC system coordinator 2. The model information includes model type and model parameter information.

[0157] 8. MEC system coordinator 2 aggregates the received model information.

[0158] 9. The MEC system coordinator 2 sends the aggregated model information to each MEC system participant.

[0159] 10. Each MEC participant updates the local model information.

[0160] Figure 9 A schematic diagram of the structure of a MEC federated learning device provided in an embodiment of the present application is shown as follows: Figure 9 As shown, the apparatus 900 may include: an MEC system coordinator determination module 901, a model information receiving module 902, a first model information aggregation module 903, and a second model information aggregation module 904, wherein:

[0161] The MEC system coordinator determination module 901 is used to determine the MEC system coordinator from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group when any multi-access edge computing MEC system in any federated learning group sends a federated learning request;

[0162] The model information receiving module 902 is used to receive model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtain the resource capacity utilization of the MEC system coordinator in real time during the receiving process;

[0163] The first model information aggregation module 083 is used to aggregate the model information through the MEC system coordinator if the resource capacity utilization rate of the MEC system coordinator does not exceed the preset threshold during the receiving process, and then send the aggregated model information to each MEC system in the federated learning group;

[0164] The second model information aggregation module 904 is used to determine a new MEC system coordinator from each MEC system and switch to receiving each model information through the new MEC system coordinator if the resource capacity utilization of the MEC system coordinator exceeds a preset threshold during the receiving process. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization of the new MEC system coordinator does not exceed the preset threshold during the receiving process. After aggregating each model information through the new MEC system coordinator, the aggregated model information is sent to each MEC system in the federated learning group.

[0165] The solution provided in this application determines the MEC system coordinator based on the resource capacity utilization of each MEC system in the group that initiates the federated learning request. In the subsequent process of receiving model information of other MEC systems through the MEC system coordinator, the resource capacity utilization of the MEC system coordinator during the reception process is obtained in real time, and the MEC system coordinator is again determined based on the resource capacity utilization whether to switch the MEC system coordinator. This solution implements MEC federated learning and takes resource capacity utilization into account during the MEC system coordinator confirmation process, thereby ensuring the performance of the MEC system coordinator and achieving balanced resource capacity utilization of the MEC system within the MEC system federated learning group.

[0166] In an optional embodiment of the present application, the MEC system coordinator determination module is specifically configured to:

[0167] Obtain a preset set of available MEC system coordinators, where each MEC system in the preset set of available MEC system coordinators carries a corresponding priority;

[0168] Among the MEC systems in the federated learning group that belong to the set of available MEC system coordinators, the MEC system with the highest priority and resource capacity utilization rate not exceeding a preset threshold is determined as the MEC system coordinator.

[0169] In an optional embodiment of the present application, the MEC system coordinator determination module is further configured to:

[0170] If the resource capacity utilization rates of all MEC systems in the federated learning group that belong to the set of available MEC system coordinators exceed a preset threshold, the MEC system with the lowest resource capacity utilization rate among the MEC systems in the federated learning group that do not belong to the set of available MEC system coordinators will be determined as the MEC system coordinator.

[0171] In an optional embodiment of the present application, the MEC system coordinator determination module is further configured to:

[0172] Obtain multiple federated learning groups from a preset number of MEC systems;

[0173] The MEC systems that appear in any federated learning group are determined as elements of the available MEC coordinator set, and the available MEC coordinator set is constructed. The priority of each MEC system in the MEC coordinator set is proportional to the number of times it appears in each federated learning group.

[0174] In an optional embodiment of the present application, the MEC system coordinator determination module is further configured to:

[0175] Identify MEC systems with the same data source as a federated learning group; and / or,

[0176] The MEC systems whose model feature similarity meets the preset conditions are identified as a federated learning group.

[0177] In an optional embodiment of the present application, the model information receiving module is specifically configured to:

[0178] Determine whether the federated learning group includes an existing MEC system coordinator;

[0179] If not included, the model information sent by other MEC systems in the federated learning group will be directly received through the MEC system coordinator;

[0180] If included, the model information received by the existing MEC system coordinator is obtained through the MEC system coordinator, and the model information of other MEC systems is continued to be received through the MEC system.

[0181] In an optional embodiment of the present application, the second model information aggregation module is specifically configured to:

[0182] Based on the resource capacity utilization of each MEC system in the federated learning group, a new MEC system coordinator is determined from each MEC system;

[0183] The model information received by the MEC system coordinator is obtained through the new MEC system coordinator, and the model information of other MEC systems is continued to be received through the new MEC system.

[0184] Reference below Figure 10 , which shows an electronic device suitable for implementing the embodiments of the present application (for example, Figure 1 The electronic devices in the embodiments of the present application may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), wearable devices, and fixed terminals such as digital TVs and desktop computers. Figure 10 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0185] The electronic device includes: a memory and a processor, wherein the memory is used to store a program for executing the methods described in each of the above method embodiments; and the processor is configured to execute the program stored in the memory. The processor here may be referred to as the processing device 1001 described below, and the memory may include at least one of the read-only memory (ROM) 1002, the random access memory (RAM) 1003, and the storage device 1008 described below, as shown below:

[0186] like Figure 10 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of the electronic device 1000 are also stored in the RAM 1003. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0187] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Figure 10 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0188] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 1009, or installed from the storage device 1008, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present application are performed.

[0189] It should be noted that the computer-readable storage medium mentioned above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0190] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0191] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0192] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0193] When any multi-access edge computing MEC system in any federated learning group issues a federated learning request, a MEC system coordinator is determined from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group. The model information sent by other MEC systems in the federated learning group is received through the MEC system coordinator, and the resource capacity utilization of the MEC system coordinator is obtained in real time during the receiving process. If the resource capacity utilization of the MEC system coordinator does not exceed the preset threshold during the receiving process, the MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group. If the resource capacity utilization of the MEC system coordinator exceeds the preset threshold during the receiving process, a new MEC system coordinator is determined from each MEC system and the model information is received through the new MEC system coordinator. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization of the new MEC system coordinator does not exceed the preset threshold during the receiving process. The new MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group.

[0194] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0195] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0196] The modules or units described in the embodiments of the present application may be implemented in software or hardware. The name of a module or unit does not, in some cases, limit the unit itself. For example, a first program switching module may also be described as a "module for switching the first program."

[0197] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0198] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0199] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific method implemented when the computer-readable medium described above is executed by an electronic device can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0200] The present invention provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, so that when the computer device executes the computer instructions, the following conditions are achieved:

[0201] When any multi-access edge computing MEC system in any federated learning group issues a federated learning request, a MEC system coordinator is determined from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group. The model information sent by other MEC systems in the federated learning group is received through the MEC system coordinator, and the resource capacity utilization of the MEC system coordinator is obtained in real time during the receiving process. If the resource capacity utilization of the MEC system coordinator does not exceed the preset threshold during the receiving process, the MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group. If the resource capacity utilization of the MEC system coordinator exceeds the preset threshold during the receiving process, a new MEC system coordinator is determined from each MEC system and the model information is received through the new MEC system coordinator. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization of the new MEC system coordinator does not exceed the preset threshold during the receiving process. The new MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group.

[0202] The above descriptions are only partial embodiments of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A MEC federated learning method, characterized in that: include: When any multi-access edge computing MEC system in any federated learning group issues a federated learning request, the MEC system coordinator is determined from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group; Receive model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtain the resource capacity utilization rate of the MEC system coordinator in real time during the receiving process; If the resource capacity utilization rate of the MEC system coordinator does not exceed a first preset threshold during the receiving process, the MEC system coordinator aggregates the model information and sends the aggregated model information to each MEC system in the federated learning group respectively; If the resource capacity utilization rate of the MEC system coordinator exceeds the first preset threshold value during the receiving process, a new MEC system coordinator is determined from each MEC system and switched to the new MEC system coordinator to receive each model information. The determination and switching of the new MEC system coordinator are repeated until the resource capacity utilization rate of the new MEC system coordinator does not exceed the first preset threshold value during the receiving process. After the model information is aggregated by the new MEC system coordinator, the aggregated model information is sent to each MEC system in the federated learning group respectively. The determining of the MEC system coordinator from each MEC system based on the resource capacity utilization of each MEC system in the federated learning group includes: Obtain a preset set of available MEC system coordinators, where each MEC system in the preset set of available MEC system coordinators carries a corresponding priority; Among the MEC systems in the federated learning group that belong to the set of available MEC system coordinators, the MEC system with a resource capacity utilization rate not exceeding a second preset threshold and with the highest priority is determined as the MEC system coordinator, and the second preset threshold is not greater than the first preset threshold.

2. The method according to claim 1, characterized in that The method further comprises: If the resource capacity utilization rates of all MEC systems in the federated learning group that belong to the set of available MEC system coordinators exceed the second preset threshold, the MEC system with the lowest resource capacity utilization rate among the MEC systems in the federated learning group that do not belong to the set of available MEC system coordinators will be determined as the MEC system coordinator.

3. The method according to claim 1, characterized in that The obtaining of a preset available MEC system coordinator set includes: Obtain multiple federated learning groups from a preset number of MEC systems; The MEC system appearing in any federated learning group is determined as an element of the available MEC coordinator set, and the available MEC coordinator set is constructed, and the priority of each MEC system in the MEC coordinator set is proportional to the number of times it appears repeatedly in each federated learning group.

4. The method according to claim 3, characterized in that The method of obtaining multiple federated learning groups from a preset number of MEC systems includes: Identify MEC systems with the same data source as a federated learning group; and / or, The MEC systems whose model feature similarity meets the preset conditions are identified as a federated learning group.

5. The method according to claim 1, wherein The receiving, through the MEC system coordinator, model information sent by other MEC systems in the federated learning group includes: Determining whether the federated learning group includes an existing MEC system coordinator; If not included, the model information sent by other MEC systems in the federated learning group is directly received through the MEC system coordinator; If included, the model information received by the existing MEC system coordinator is obtained through the MEC system coordinator, and the model information of other MEC systems is continued to be received through the MEC system.

6. The method according to claim 1, characterized in that The determining of a new MEC system coordinator from each MEC system and switching to the new MEC system coordinator to receive each model information includes: Determine the new MEC system coordinator from each MEC system based on resource capacity utilization of each MEC system in the federated learning group; The model information received by the MEC system coordinator is obtained through the new MEC system coordinator, and the model information of other MEC systems is continued to be received through the new MEC system.

7. A MEC federated learning device, characterized in that: include: An MEC system coordinator determination module is configured to determine an MEC system coordinator from each MEC system based on resource capacity utilization of each MEC system in any federated learning group when any multi-access edge computing MEC system in any federated learning group issues a federated learning request; A model information receiving module is configured to receive model information sent by other MEC systems in the federated learning group through the MEC system coordinator, and obtain the resource capacity utilization of the MEC system coordinator in real time during the receiving process; A first model information aggregation module is configured to aggregate the model information through the MEC system coordinator if the resource capacity utilization rate of the MEC system coordinator does not exceed a first preset threshold during the receiving process, and then send the aggregated model information to each MEC system in the federated learning group; A second model information aggregation module is configured to determine a new MEC system coordinator from each MEC system and switch to the new MEC system coordinator to receive each model information if the resource capacity utilization of the MEC system coordinator exceeds the first preset threshold during the receiving process, repeat the determination and switching of the new MEC system coordinator until the resource capacity utilization of the new MEC system coordinator does not exceed the first preset threshold during the receiving process, aggregate each model information through the new MEC system coordinator, and send the aggregated model information to each MEC system in the federated learning group respectively; The MEC system coordinator determination module is specifically used to: Obtain a preset set of available MEC system coordinators, where each MEC system in the preset set of available MEC system coordinators carries a corresponding priority; Among the MEC systems in the federated learning group that belong to the set of available MEC system coordinators, the MEC system with a resource capacity utilization rate not exceeding a second preset threshold and with the highest priority is determined as the MEC system coordinator, and the second preset threshold is not greater than the first preset threshold.

8. An electronic device, characterized in that: including memory and processor; The memory stores a computer program; The processor is configured to execute the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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